A First Look Inside Peer39 & Its Semantic Advertising Technology

No venture capitalist’s week is complete without seeing a company in the semantic space holding steadfast to the assertion that its pre-money Round A valuation is a steal at $50 million. The situation wouldn’t be totally absurd if it were not for the facts that these technologies are mostly half-baked, or far from becoming a deployable product. And they are most definitely leaps and bounds from being able to generate revenue—let alone actually generating revenue at the time of their pre-money $50 million valuation pitch.

But in the middle of this circus is a modest and press-shy company called Peer39. The company, which has purposefully been under the publicity radar since its inception in 2006, is a promising example that semantic technologies have true monetization potential. Peer39 is demonstrating this thoroughly through semantic advertising, the next evolutionary step beyond behavioral advertising, which was preceded of course by contextual advertising.

Peer39 is spearheading this space with its semantic advertising network, which is on track to hit one billion impressions by the end of the year. Their network only handles display ads at the moment, charging advertisers based on a CPM (cost per thousand) or CPC (cost per click) basis.

In an interview with TechCrunch, Amiad Solomon, the company’s CEO, claims that through the networks’ 30 top-level categories and thousands of sub-categories, Peer39 is providing advertisers, on average, clickthrough-rate improvements of four times what they were getting before. Publishers hail from all verticals, including technology, automotive, finance, and health.

Powering Peer39’s ad network is SemanticMatch, an algorithmic engine able to perform precise matching of commercial offers with user-generated content content (blogs, forums, etc.). SemanticMatch employs natural language processing and machine learning to determine meanings, topics, categories and even sentiment. All this, mind you, is done in real-time and Peer39 does not employ cookies in any way, nipping many privacy-related concerns in the bud.

Peer39’s technology has been two and a half years in the making. The 44-strong Israeli R&D team includes the former VP of R&D at Nielsen Online, as well as scientists from the Technion Institute (Israel’s MIT) and the Israeli army’s prestigious intelligence units. Together they were able to tackle the challenges of breaking down text into small enough pieces to categorize text with algorithms that are 1) generic enough to be applicable across a wide variety of content categories, and 2) able to adapt themselves to new categories on-the-fly.

Unlike contextual analysis that seeks out key words, Peer39’s semantic analysis engine—SemanticMatch—looks at the entire page to derive its meaning and its relevant categories. Below are a couple of examples provided by the company (I’ve added the italics for emphasis):

Peer39 is also able to determine positive/negative sentiment. This is important because it allows Peer39 to offer its advertisers various brand protection thresholds, such as offensive content, negative content, mention of competitors, etc. Canon, for example, might not want to advertise on a page with a negative review of one of its cameras.

On the business execution level Peer39 is attempting to pull off a Tacoda or a Quigo exit. Both companies were able to develop sizeable ad networks by delivering quality ROI results and working closely with agencies.

The company has raised $11.7 million from Canaan Venture Partners, Dawntreader Ventures, and JP Morgan. (The company won’t disclose its pre-money valuation, but given how much it has raised, a valuation north of $50 million is a safe bet). Its board of advisers includes Eytan Elbaz, a co-founder of Applied Semantics (which was bought by Google and became the basis for AdSense) and the former president of Tacoda Daniel Jaye. Also, the company just announced that Matthew Goldestein, former SVP of Revenue Operations at Tacoda joined as COO.